The recent advances in natural language processing (NLP) are linked to training processes that require vast amounts of corpora. Access to this data is commonly not a trivial process due to resource dispersion and the need to maintain these infrastructures online and up-to-date. New developments in NLP are often compromised due to the scarcity of data or lack of a shared repository that works as an entry point to the community. This is especially true in low and mid-resource languages, such as Portuguese, which lack data and proper resource management infrastructures. In this work, we propose PT-Pump-Up, a set of tools that aim to reduce resource dispersion and improve the accessibility to Portuguese NLP resources. Our proposal is divided into four software components: a) a web platform to list the available resources; b) a client-side Python package to simplify the loading of Portuguese NLP resources; c) an administrative Python package to manage the platform and d) a public GitHub repository to foster future collaboration and contributions. All four components are accessible using: https://linktr.ee/pt_pump_up

Link analysis is a technique frequently used in the ranking of web sites. On the web, we often encounter content that is organized by entries, sorted from recent to old, and generally follows the structure of a blog. In this paper we explore and evaluate the usage of a bibliometrics measure, called h-index, for the task of blog ranking, in an information retrieval context. We base our experiments on the TREC Blogs08 collection, which comprises over 28 million posts. The results obtained indicate that the h-index is a robust metric that allows for an improved relevance discrimination between blogs, when compared to the in-degree. Additionally, tests performed using distinct versions of the post graph, indicate that this metric might tolerate a certain level of link clutter.